Are you looking to learn R but don't know where to start?
Well, look no further.
We thought we'd make it easier for you to find out where to find FREE R Language books and we've listed all the greatest and FREE books on R programming in one single unmissable blog post.
Ready to gain new R programming Data Analysis skills?
In this post we bring you all the FREE books that combine R programming with Data Science that we've found (so far), categorised by sub-topic so you can find what you're looking for easily.
We'll be adding more in to this page regularly, so bookmark the page and keep popping in regularly to see what's new.
To download your FREE R Language books, click on the book images you're interested in and you'll be directed to the page where you can read or download them.
Just so you know, since a lot of these R programming books are essential and we know some of you would love to have hard copies of them as well, we've added links to books that are available in hard copy format.
Disclosure: The FREE ebooks were free to download at the time of posting but other links in this post may contain affiliate links. As Amazon Associates we may earn from qualifying purchases.
You can find further details in our TCs
FREE General R Programming Books
The R Inferno
An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.
This book will help you become an effective R programmer.
Based on the author's experience, the book will help you avoid the mistakes and dead ends, and will teach you useful tools, techniques, and idioms that can help you to attack many types of problems.
Aimed at intermediate R programmers who want to dive deeper into R and learn new strategies for solving diverse problems.
FREE Data Science Books with R Programming
Developing Data Products In R
This book covers the exciting field of data products. A data product is the output of a data science experiment. In this book we focus on developing data products in R, the most popular language for data scientists. Special emphasis is given to developing Shiny apps. Shiny is a platform for developing web front ends for back end R calculations. Shiny allows users to create polished web apps while only knowing R and a little bit of html.
After shiny we cover other ways to make reproducible presentations and interactive graphics. In reproducible presentations, we cover Slidify and RStudio's Presenter. In interactive graphics, we demonstrate RCharts, Leaflet, googleVis and plot.ly.
Data Mining Algorithms in R
Most of the data mining literature is too abstract regarding the actual use of the algorithms and parameter tuning is usually a frustrating task. On the other hand, there is a large number of implementations available, such as those in the R project, but their documentation focus mainly on implementation details without providing a good discussion about parameter-related trade-offs associated with each of them.
This Wikibook aims to fill this gap by integrating three pieces of information for each technique: description and rationale, implementation details, and use cases.
Mastering Software Development In R
This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)
Report Writing For Data Science In R
This book teaches the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations....
Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available.
This book will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
R for Data Science
Hadley Wickham and Garret Grolemund
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
R Programming for Data Science
Roger D. Peng
This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science.
FREE R Programming Books for Statistics
A Little Book of R For Time Series
This is a simple introduction to time series analysis using the R statistics software (have you spotted the pattern yet?). It includes instruction on how to read and plot time series, time series decomposition, forecasting, and ARIMA models
A Little Book Of R For Multivariate Analysis
A Little Book of R for Multivariate Analysis is a simple introduction to multivariate analysis using the R statistics software.
It covers topics such as reading and plotting multivariate data, principal components analysis, and linear discriminant analysis.
It's only 49 pages long and you can read it online or download it as a pdf.
Practical Regression and Anova Using R
Julian J. Faraway
The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.
This book is not for beginners. It presumes some knowledge of basic statistical theory and practice, such as statistical inference like estimation, hypothesis testing and confidence intervals. A basic knowledge of data analysis is presumed. Some linear algebra and calculus is also required..
Data analysis And Graphics Using R
John Maindonald and W. John Braun
Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data.
The many worked examples, from real-world research, are accompanied by commentary on what is done and why.
introduction to statistical thought
"Statistical thinking" means a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus.
The book uses computer calculation and simulation as another way of explaining and helping readers understand the underlying concepts. The book is written with the statistical language R embedded throughout.
from algorithms to z-scores: probabilistic and statistical modeling in computer science
The materials here form a textbook for a course in mathematical probability and statistics for computer science students.
Throughout the units, mathematical theory and applications are interwoven, with a strong emphasis on modelling.
an introduction to statistical learning with applications in r
Gareth James, Daniella Witten, Trevor Hastie and Robert Tibshirani
This book provides an introduction to statistical learning methods.
It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
Forecasting Principles and Practice
Rob J. Hyndman and George Athanasopoulos
Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.
I hope that these FREE R books whet your appetite for more learning - especially as they're all free!
Is there a book that you'd like to recommend for this list? Are any of the links out of date? Leave a comment at the bottom and I'll jump right on it.
Bookmark this page so you can return to it and pick up more free R programming books, enjoy and don't forget to share with your friends on social media!
If you're looking for more FREE Data Science Books we also have the following posts.
- Fantastic Free Data Science Books for Aspiring Data Scientists
- Free Must-Read Statistics Books for Aspiring Data Scientists
- Free Essential Python Books for Aspiring Data Scientists
- Free Books on R Programming That all Aspiring Data Scientists Should Read
- Free Inspirational Machine Learning Books for Aspiring Data Scientists